Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction
This addresses drug sensitivity prediction for precision oncology, but it is incremental as it applies an existing method to new data.
The study tackled predicting anti-cancer drug sensitivity using GPT-3 with structured pharmacogenomics data, achieving results that could lead to more efficient treatment protocols in precision oncology.
In this study, we investigated the potential of GPT-3 for the anti-cancer drug sensitivity prediction task using structured pharmacogenomics data across five tissue types and evaluated its performance with zero-shot prompting and fine-tuning paradigms. The drug's smile representation and cell line's genomic mutation features were predictive of the drug response. The results from this study have the potential to pave the way for designing more efficient treatment protocols in precision oncology.